Fetching the paper…
Reading the bibliography…
Transformer-based large language models (LLM) have been widely used in language processing applications.
Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. 2019 · 1901
Earlier work this paper cites.
Revealing the dark secrets of bert
Olga Kovaleva, Alexey Romanov, Anna Rogers, and Anna Rumshisky. 2019 · 1908
Earlier work this paper cites.
Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu. 2019 · 1909
Earlier work this paper cites.
Lamol: Language modeling for lifelong language learning
Fan-Keng Sun, Cheng-Hao Ho, and Hung-Yi Lee. 2019 · 1909
Earlier work this paper cites.
Compressive transformers for long-range sequence modelling
Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, and Timothy P Lillicrap. 2019 · 1911
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya. 2020 · 2001
Earlier work this paper cites.
Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2004
Earlier work this paper cites.
Multilevel generalized force-directed method for circuit placement
Tony Chan, Jason Cong, and Kenton Sze. 2005 · 2005
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020 · 2010
Earlier work this paper cites.
Memformer: A memory-augmented transformer for sequence modeling
Qingyang Wu, Zhenzhong Lan, Kun Qian, Jing Gu, Alborz Geramifard, and Zhou Yu. 2020 · 2010
Earlier work this paper cites.
Epistemic information in stratified m-spaces
Mark Burgin. 2011 · 2011
Earlier work this paper cites.
Multilevel optimization in VLSICAD , volume 14
Jingsheng Jason Cong and Joseph R Shinnerl. 2013 · 2013
Earlier work this paper cites.
A focused backpropagation algorithm for temporal pattern recognition
Michael C Mozer. 2013 · 2013
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Big data for natural language processing: A streaming approach
Rodrigo Agerri, Xabier Artola, Zuhaitz Beloki, German Rigau, and Aitor Soroa. 2015 · 2015
Earlier work this paper cites.
Recurrent neural networks hardware implementation on fpga
Andre Xian Ming Chang, Berin Martini, and Eugenio Culurciello. 2015 · 2015
Cited alongside, same era.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Cited alongside, same era.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020 · 2020
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao. 2023 · 2023
Later among the works it cites.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
Later among the works it cites.
Compressed context memory for online language model interaction
Jang-Hyun Kim, Junyoung Yeom, Sangdoo Yun, and Hyun Oh Song. 2023 · 2023
Later among the works it cites.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
Later among the works it cites.
Ret-llm: Towards a general read-write memory for large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Cited alongside, same era.
Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Shuangfei Zhai, Walter Talbott, Nitish Srivastava, Chen Huang, Hanlin Goh, Ruixiang Zhang, and Josh Susskind. 2021 · 2021
Cited alongside, same era.
Poolingformer: Long document modeling with pooling attention
Hang Zhang, Yeyun Gong, Yelong Shen, Weisheng Li, Jiancheng Lv, Nan Duan, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Recurrent memory transformer
Aydar Bulatov, Yury Kuratov, and Mikhail Burtsev. 2022 · 2022
Cited alongside, same era.
Lifelong learning for question answering with hierarchical prompts
Yi Dai, Hao Lang, Yinhe Zheng, Fei Huang, Luo Si, and Yongbin Li. 2022 · 2022
Cited alongside, same era.
Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz, and Hinrich Schütze. 2023 · 2023
Later among the works it cites.
Efficient memory-enhanced transformer for long-document summarization in low-resource regimes
Gianluca Moro, Luca Ragazzi, Lorenzo Valgimigli, Giacomo Frisoni, Claudio Sartori, and Gustavo Marfia. 2023 · 2023
Later among the works it cites.
Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Later among the works it cites.
Smollm - blazingly fast and remarkably powerful
Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Leandro von Werra, and Thomas Wolf. 2024 · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
Closest in time.
Jamba: A hybrid transformer-mamba language model
Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, et al. 2024 · 2024
Closest in time.
Hierarchical context merging: Better long context understanding for pre-trained llms
Woomin Song, Seunghyuk Oh, Sangwoo Mo, Jaehyung Kim, Sukmin Yun, Jung-Woo Ha, and Jinwoo Shin. 2024 · 2024
Closest in time.
Jamba-1.5: Hybrid transformer-mamba models at scale
Jamba Team, Barak Lenz, Alan Arazi, Amir Bergman, Avshalom Manevich, Barak Peleg, Ben Aviram, Chen Almagor, Clara Fridman, Dan Padnos, et al. 2024 · 2024
Closest in time.
Augmenting language models with long-term memory
Weizhi Wang, Li Dong, Hao Cheng, Xiaodong Liu, Xifeng Yan, Jianfeng Gao, and Furu Wei. 2024 · 2024
Closest in time.
Memory3: Language modeling with explicit memory
Hongkang Yang, Zehao Lin, Wenjin Wang, Hao Wu, Zhiyu Li, Bo Tang, Wenqiang Wei, Jinbo Wang, Zeyun Tang, Shichao Song, et al. 2024 · 2024
Closest in time.
Yi: Open foundation models by 01. ai
Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al. 2024 · 2024
Closest in time.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2024
Closest in time.